Dynamic factor models for binary data in circular spaces: an application to the US Supreme Court
提出一种基于圆形潜在空间的动态因子模型，用于分析美国最高法院大法官的投票行为，发现1937至2021年间多数投票模式更适合用圆形空间描述。
Abstract Latent factor models are widely used in the social and behavioural sciences as scaling tools to map discrete multivariate outcomes into low-dimensional, continuous scales. In political science, dynamic versions of classical factor models have been widely used to study the evolution of justices’ preferences in multi-judge courts. In this paper, we discuss a new dynamic factor model that relies on a latent circular space that can accommodate voting behaviours in which justices commonly understood to be on opposite ends of the ideological spectrum vote together on a substantial number of otherwise closely divided opinions. We apply this model to data on nonunanimous decisions made by the US Supreme Court between 1937 and 2021, and show that for most of this period, voting patterns can be better described by a circular latent space.